Sparse Representation of Measurements

1. A method of performing a sparsity technique, comprising: by a computer system: updating a dictionary of predetermined features based at least in part on non-invasive measurements performed on an individual and historical non-invasive measurements; determining weights associated with features in an updated dictionary of predetermined features based at least in part on the non-invasive measurements; computing or selecting a sampling pattern based at least in part on the non-invasive measurements and the historical non-invasive measurements; obtaining an image of at least a portion of the individual by performing additional non-invasive measurements based at least in part on the computed or selected sampling pattern, wherein the image comprises a sub-sampled or a compressed image; and reconstructing a second image based at least in part on the image, the updated dictionary of predetermined features and the determined weights.

2. The method of claim 1, wherein the non-invasive measurements and the historical non-invasive measurements comprise or correspond to magnetic-resonance (MR) measurements.

3. The method of claim 1, wherein the updating comprises performing a minimization technique with a cost function having an L2-norm term and an L0-norm term.

4. The method of claim 1, wherein the non-invasive measurements and the historical non-invasive measurements comprises magnetic-resonance (MR) parameters associated with voxels in the individual.

5. The method of claim 4, wherein the MR parameters comprise: a density of a type of nuclei, a longitudinal relaxation time along a direction parallel to an external magnetic field and a transverse relaxation time along a direction perpendicular to the external magnetic field.

6. The method of claim 4, wherein the non-invasive measurement comprise at least a component of a magnetization associated with the individual, and the method comprises: calculating at least a predicted component of the magnetization for the voxels associated with the individual based at least in part on the measured component of the magnetization, a forward model, an external magnetic field and a radio frequency (RF) pulse sequence; and solving an inverse problem by iteratively modifying the MR parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value.

7. The method of claim 1, wherein the historical non-invasive measurements are associated with the individual or a group of individuals.

8. The method of claim 7, wherein the group of individuals excludes the individual.

9. The method of claim 1, wherein determining the weights comprises a gradient-descent technique.

10. The method of claim 1, wherein the updated dictionary of predetermined features correspond to a portion of an anatomy of the individual.

11. A computer system, comprising: an interface circuit; a processor coupled to the interface circuit; and memory, coupled to the processor, storing program instructions, wherein, when executed by the processor, the program instructions cause the computer system to perform operations comprising: updating a dictionary of predetermined features based at least in part on non-invasive measurements performed on an individual and historical non-invasive measurements; determining weights associated with features in the updated dictionary of predetermined features based at least in part on the non-invasive measurements; computing or selecting a sampling pattern based at least in part on the non-invasive measurements and the historical non-invasive measurements; obtaining an image of at least a portion of the individual by performing additional non-invasive measurements based at least in part on the computed or selected sampling pattern, wherein the image comprises a sub-sampled or a compressed image; and reconstructing a second image based at least in part on the image, the updated dictionary of predetermined features and the determined weights.

12. The computer system of claim 11, wherein the non-invasive measurements and the historical non-invasive measurements comprise or correspond to magnetic-resonance (MR) measurements.

13. The computer system of claim 11, wherein the non-invasive measurements and the historical non-invasive measurements comprises magnetic-resonance (MR) parameters associated with voxels in the individual.

14. The computer system of claim 11, wherein the updating comprises performing a minimization technique with a cost function having an L2-norm term and an L0-norm term.

15. The computer system of claim 13, wherein the non-invasive measurement comprise at least a component of a magnetization associated with the individual, and the operations comprise: calculating at least a predicted component of the magnetization for the voxels associated with the individual based at least in part on the measured component of the magnetization, a forward model, an external magnetic field and a radio frequency (RF) pulse sequence; and solving an inverse problem by iteratively modifying the MR parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value.

16. The computer system of claim 11, wherein the historical non-invasive measurements are associated with the individual or a group of individuals; and wherein the group of individuals excludes the individual.

17. The computer system of claim 11, wherein determining the weights comprises a gradient-descent technique.

18. The computer system of claim 11, wherein the updated dictionary of predetermined features correspond to a portion of an anatomy of the individual.

19. A non-transitory computer-readable storage medium for use in conjunction with a computer system, the computer-readable storage medium configured to store a program module that, when executed by the computer system, causes the computer system to perform operations comprising: updating a dictionary of predetermined features based at least in part on non-invasive measurements performed on an individual and historical non-invasive measurements; determining weights associated with features in the updated dictionary of predetermined features based at least in part on the non-invasive measurements; computing or selecting a sampling pattern based at least in part on the non-invasive measurements and the historical non-invasive measurements; obtaining an image of at least a portion of the individual by performing additional non-invasive measurements based at least in part on the computed or selected sampling pattern, wherein the image comprises a sub-sampled or a compressed image; and reconstructing a second image based at least in part on the image, the updated dictionary of predetermined features and the determined weights.

20. The computer-readable storage medium of claim 19, wherein the non-invasive measurements and the historical non-invasive measurements comprises magnetic-resonance (MR) parameters associated with voxels in the individual.